Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.
Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.
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临床上针对肿瘤转录组靶向和免疫疗法的患者反应的临床预测。
DOI：
10.1016/j.medj.2022.11.001
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发表时间：
2023-01-13
期刊：
影响因子：
17
通讯作者：
Aharonov, Ranit
中科院分区：
文献类型：
--
作者：
Dinstag, Gal;Shulman, Eldad D.;Elis, Efrat;Ben-Zvi, Doreen S.;Tirosh, Omer;Maimon, Eden;Meilijson, Isaac;Elalouf, Emmanuel;Temkin, Boris;Vitkovsky, Philipp;Schiff, Eyal;Hoang, Danh-Tai;Sinha, Sanju;Nair, Nishanth Ulhas;Lee, Joo Sang;Schaffer, Alejandro A.;Ronai, Ze'ev;Juric, Dejan;Apolo, Andrea B.;Dahut, William L.;Lipkowitz, Stanley;Berger, Raanan;Kurzrock, Razelle;Papanicolau-Sengos, Antonios;Karzai, Fatima;Gilbert, Mark R.;Aldape, Kenneth;Rajagopal, Padma S.;Beker, Tuvik;Ruppin, Eytan;Aharonov, Ranit
精确肿瘤学正在逐步进入主流临床实践，显示出显著的生存益处。然而，在许多情况下，合格和应答率仍然有限，需要更好的预测性生物标志物。我们提出了Enlight，这是一种基于转录学的计算方法，它识别临床相关的基因相互作用，并使用它们来预测患者对多种癌症类型的各种治疗的反应，而不需要训练以前的治疗反应数据。我们在两个面向翻译的场景中研究Enlight：个性化肿瘤学(PO)，旨在为单个患者确定治疗优先级；临床试验设计(CTD)，在患者队列中选择最有可能的应答者。在PO设置下评估Enlight在21个盲目临床试验数据集上的性能，我们表明它可以有效地预测患者对多种治疗方法和癌症类型的治疗反应。它的预测精度比以前发表的基于转录组学的签名更好，并与针对特定适应症和药物开发的监督预测器相媲美。结合干扰素-γ签名，光线公司在预测对免疫检查点治疗的反应方面的赔率比超过4。在CTD方案中，光线可以通过排除无应答者，潜在地提高免疫疗法和其他单抗的临床试验成功，同时总体上实现最佳排斥策略下90%以上的应答率。Enlight明显增强了从大宗肿瘤转录组预测多种癌症类型治疗反应的能力。这项研究得到了NIH校内研究计划和以色列创新局的部分支持。Dinstag等人。描述Enlight，一种基于转录学的计算方法，从肿瘤转录组中识别临床相关的基因相互作用。与已公布的生物标记物相比，Enlight可以更好地预测多种疗法和癌症类型的治疗反应，并且通过有效地排除无反应者，它有可能提高临床试验的成功。
Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient’s response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. Evaluating ENLIGHT’s performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient’s treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority. Dinstag et al. describe ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions from the tumor transcriptome. ENLIGHT can predict treatment response across multiple therapies and cancer types better than published biomarkers, and it can potentially enhance clinical trial success by effectively excluding non-responders.
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